Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
By
Longqi Liu,
Chuanyu Liu,
Andrés Quintero,
Liang Wu,
Yue Yuan,
Mingyue Wang,
Mengnan Cheng,
Lizhi Leng,
Liqin Xu,
Guoyi Dong,
Rui Li,
Yang Liu,
Xiaoyu Wei,
Jiangshan Xu,
Xiaowei Chen,
Haorong Lu,
Dongsheng Chen,
Quanlei Wang,
Qing Zhou,
Xinxin Lin,
Guibo Li,
Shiping Liu,
Qi Wang,
Hongru Wang,
J. Lynn Fink,
Zhengliang Gao,
Xin Liu,
Yong Hou,
Shida Zhu,
Huanming Yang,
Yunming Ye,
Ge Lin,
Fang Chen,
Carl Herrmann,
Roland Eils,
Zhouchun Shang,
Xun Xu
Posted 19 May 2018
bioRxiv DOI: 10.1101/316208
(published DOI: 10.1038/s41467-018-08205-7)
Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. By applying our integrated approach to multiple cancer cell lines, we discovered genomic loci with coordinated epigenomic and transcriptomic variability. In addition, decomposition of combined single-cell chromatin accessibility and gene expression features by a non-negative matrix factorization (NMF) based method identified signatures reflecting cell type specificity and revealed a profound regulatory relationship between the two layers of omics. We further characterized subpopulations associated with distinct regulatory patterns within patient-derived xenograft models and discovered epigenomic and transcriptomic clues that drive tumor heterogeneity. The ability to obtain these two layers of omics data will help provide more accurate definitions of "single cell states" and enable the deconvolution of regulatory heterogeneity from complex cell populations.
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